Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis. Dynamic systems are represented by dynamic graphs with spatial and temporal dimensions. As objects and their relations in a dynamic graph change over time, detecting these changes is essential. Numerous methods for change point detection in dynamic graphs have been developed, but no systematic review exists. This paper addresses this gap by introducing change point detection tasks in dynamic graphs, discussing two tasks based on input data types: detection in graph snapshot series (focusing on graph topology changes) and time series on graphs (focusing on changes in graph entities with temporal dynamics). We then present related challenges and applications, provide a comprehensive taxonomy of surveyed methods, including datasets and evaluation metrics, and discuss promising research directions.

In China, the rapid development of medical informatization and the increasing public health awareness greatly stimulate the growth of Electronic Physical Records (EPR). This paper focuses on analyzing and mining electronic physical records to provide constructive suggestions for people's lifestyles using data of thirty thousand individual records in 2009 within the nine-month period from April to December, at China-Japan Union Hospital of Jilin University. During the analyzing process, numerous challenges were faced such as data storage, data noise and data incompletion etc. Thus we designed a framework as the resolution to these problems, and utilized data mining methods including association rules to find the characteristics of all ages in Changchun and gained some satisfactory results such as the abnormal BMI increasing with age. We also utilized the community discovery algorithm to explore the relationship between abnormal physical indicators. However, the result is not satisfactory and the module cant even reach 0.1. To solve the problem of information isolation in China, we also implement a data platform.